We evaluated our approach on three widely used datasets, namely UCF sports [1]}, Olympic sports datasets [2]} and a more difficult Human Motion Database (HMDB51) dataset [3]}. Fig. REF  provides some examples of the three datasets used in this paper. UCF sports dataset contains a set of actions collected from various sports which are typically featured on broadcast channels such as ESPN or BBC. This dataset consists of 150 videos with the resolution of 720 \(\times \)  480 and with 10 different action categories in it. Olympic sports dataset was collected from YouTube sequences [2]} and contains 16 different sports categories with 50 videos per class. Hence, there are total 800 videos in this dataset. The HMDB51 dataset is a more difficult dataset which provides three train-test splits each consisting of 5100 videos. This clips are labeled with 51 action categories. The training set for each split has 3570 videos and the test set has 1530 videos.
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